Overview

Dataset statistics

Number of variables23
Number of observations58919
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.3 MiB
Average record size in memory184.0 B

Variable types

Numeric16
Categorical7

Alerts

in_transit_qty is highly correlated with forecast_6_month and 6 other fieldsHigh correlation
forecast_3_month is highly correlated with forecast_6_month and 5 other fieldsHigh correlation
forecast_6_month is highly correlated with in_transit_qty and 6 other fieldsHigh correlation
forecast_9_month is highly correlated with in_transit_qty and 6 other fieldsHigh correlation
sales_1_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
sales_3_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
sales_6_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
sales_9_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
min_bank is highly correlated with in_transit_qty and 4 other fieldsHigh correlation
perf_6_month_avg is highly correlated with perf_12_month_avgHigh correlation
perf_12_month_avg is highly correlated with perf_6_month_avgHigh correlation
in_transit_qty is highly correlated with forecast_3_month and 7 other fieldsHigh correlation
forecast_3_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
forecast_6_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
forecast_9_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
sales_1_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
sales_3_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
sales_6_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
sales_9_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
min_bank is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
perf_6_month_avg is highly correlated with perf_12_month_avgHigh correlation
perf_12_month_avg is highly correlated with perf_6_month_avgHigh correlation
forecast_3_month is highly correlated with forecast_6_month and 3 other fieldsHigh correlation
forecast_6_month is highly correlated with forecast_3_month and 5 other fieldsHigh correlation
forecast_9_month is highly correlated with forecast_3_month and 5 other fieldsHigh correlation
sales_1_month is highly correlated with forecast_3_month and 6 other fieldsHigh correlation
sales_3_month is highly correlated with forecast_3_month and 6 other fieldsHigh correlation
sales_6_month is highly correlated with forecast_6_month and 5 other fieldsHigh correlation
sales_9_month is highly correlated with forecast_6_month and 5 other fieldsHigh correlation
min_bank is highly correlated with sales_1_month and 3 other fieldsHigh correlation
perf_6_month_avg is highly correlated with perf_12_month_avgHigh correlation
perf_12_month_avg is highly correlated with perf_6_month_avgHigh correlation
lead_time is highly correlated with perf_6_month_avg and 1 other fieldsHigh correlation
in_transit_qty is highly correlated with forecast_3_month and 7 other fieldsHigh correlation
forecast_3_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
forecast_6_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
forecast_9_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
sales_1_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
sales_3_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
sales_6_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
sales_9_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
min_bank is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
perf_6_month_avg is highly correlated with lead_time and 1 other fieldsHigh correlation
perf_12_month_avg is highly correlated with lead_time and 1 other fieldsHigh correlation
national_inv is highly skewed (γ1 = 26.21876141) Skewed
pieces_past_due is highly skewed (γ1 = 25.47929635) Skewed
sku is uniformly distributed Uniform
sku has unique values Unique
national_inv has 5996 (10.2%) zeros Zeros
in_transit_qty has 47312 (80.3%) zeros Zeros
forecast_3_month has 34863 (59.2%) zeros Zeros
forecast_6_month has 31574 (53.6%) zeros Zeros
forecast_9_month has 29856 (50.7%) zeros Zeros
sales_1_month has 30495 (51.8%) zeros Zeros
sales_3_month has 23658 (40.2%) zeros Zeros
sales_6_month has 20066 (34.1%) zeros Zeros
sales_9_month has 18134 (30.8%) zeros Zeros
min_bank has 30846 (52.4%) zeros Zeros
pieces_past_due has 57335 (97.3%) zeros Zeros
perf_6_month_avg has 1600 (2.7%) zeros Zeros
perf_12_month_avg has 1232 (2.1%) zeros Zeros
local_bo_qty has 57337 (97.3%) zeros Zeros

Reproduction

Analysis started2021-10-21 16:04:10.444487
Analysis finished2021-10-21 16:04:55.477836
Duration45.03 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

sku
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct58919
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42053.60125
Minimum2
Maximum84171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size460.4 KiB
2021-10-22T00:04:55.554224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4186.9
Q121068.5
median41948
Q363047
95-th percentile80009.1
Maximum84171
Range84169
Interquartile range (IQR)41978.5

Descriptive statistics

Standard deviation24301.05717
Coefficient of variation (CV)0.5778591239
Kurtosis-1.197242328
Mean42053.60125
Median Absolute Deviation (MAD)20993
Skewness0.004193389048
Sum2477756132
Variance590541379.8
MonotonicityStrictly increasing
2021-10-22T00:04:55.696374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61411
 
< 0.1%
394301
 
< 0.1%
353321
 
< 0.1%
455711
 
< 0.1%
824291
 
< 0.1%
701351
 
< 0.1%
660371
 
< 0.1%
680841
 
< 0.1%
783231
 
< 0.1%
803701
 
< 0.1%
Other values (58909)58909
> 99.9%
ValueCountFrequency (%)
21
< 0.1%
51
< 0.1%
71
< 0.1%
91
< 0.1%
101
< 0.1%
111
< 0.1%
161
< 0.1%
191
< 0.1%
201
< 0.1%
221
< 0.1%
ValueCountFrequency (%)
841711
< 0.1%
841691
< 0.1%
841671
< 0.1%
841661
< 0.1%
841651
< 0.1%
841641
< 0.1%
841631
< 0.1%
841621
< 0.1%
841611
< 0.1%
841601
< 0.1%

national_inv
Real number (ℝ)

SKEWED
ZEROS

Distinct2492
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.4039274
Minimum-401
Maximum75813
Zeros5996
Zeros (%)10.2%
Negative713
Negative (%)1.2%
Memory size460.4 KiB
2021-10-22T00:04:55.834059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-401
5-th percentile0
Q13
median11
Q354
95-th percentile586
Maximum75813
Range76214
Interquartile range (IQR)51

Descriptive statistics

Standard deviation1529.148278
Coefficient of variation (CV)7.668596591
Kurtosis946.0195874
Mean199.4039274
Median Absolute Deviation (MAD)10
Skewness26.21876141
Sum11748680
Variance2338294.457
MonotonicityNot monotonic
2021-10-22T00:04:55.974436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05996
 
10.2%
24077
 
6.9%
33413
 
5.8%
12702
 
4.6%
42579
 
4.4%
52117
 
3.6%
61854
 
3.1%
71636
 
2.8%
101632
 
2.8%
81429
 
2.4%
Other values (2482)31484
53.4%
ValueCountFrequency (%)
-4011
< 0.1%
-2131
< 0.1%
-1522
< 0.1%
-1261
< 0.1%
-1201
< 0.1%
-1021
< 0.1%
-1002
< 0.1%
-981
< 0.1%
-931
< 0.1%
-892
< 0.1%
ValueCountFrequency (%)
758131
< 0.1%
752541
< 0.1%
731481
< 0.1%
731281
< 0.1%
730661
< 0.1%
695001
< 0.1%
667121
< 0.1%
663141
< 0.1%
543441
< 0.1%
537851
< 0.1%

lead_time
Real number (ℝ≥0)

HIGH CORRELATION

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.915697822
Minimum0
Maximum28
Zeros456
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size460.4 KiB
2021-10-22T00:04:56.105252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median8
Q38
95-th percentile12
Maximum28
Range28
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.522994714
Coefficient of variation (CV)0.5094199897
Kurtosis-0.4717713477
Mean6.915697822
Median Absolute Deviation (MAD)1
Skewness-0.01686135996
Sum407466
Variance12.41149175
MonotonicityNot monotonic
2021-10-22T00:04:56.198868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
825652
43.5%
213525
23.0%
127345
 
12.5%
44616
 
7.8%
94472
 
7.6%
3679
 
1.2%
10522
 
0.9%
0456
 
0.8%
14381
 
0.6%
16330
 
0.6%
Other values (17)941
 
1.6%
ValueCountFrequency (%)
0456
 
0.8%
11
 
< 0.1%
213525
23.0%
3679
 
1.2%
44616
 
7.8%
5186
 
0.3%
6190
 
0.3%
75
 
< 0.1%
825652
43.5%
94472
 
7.6%
ValueCountFrequency (%)
284
 
< 0.1%
262
 
< 0.1%
251
 
< 0.1%
248
 
< 0.1%
226
 
< 0.1%
213
 
< 0.1%
2024
 
< 0.1%
191
 
< 0.1%
186
 
< 0.1%
17130
0.2%

in_transit_qty
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct798
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.14075256
Minimum0
Maximum3872
Zeros47312
Zeros (%)80.3%
Negative0
Negative (%)0.0%
Memory size460.4 KiB
2021-10-22T00:04:56.320842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile55
Maximum3872
Range3872
Interquartile range (IQR)0

Descriptive statistics

Standard deviation117.7503867
Coefficient of variation (CV)6.869615922
Kurtosis320.4235556
Mean17.14075256
Median Absolute Deviation (MAD)0
Skewness15.37658738
Sum1009916
Variance13865.15356
MonotonicityNot monotonic
2021-10-22T00:04:56.564970image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
047312
80.3%
11350
 
2.3%
2764
 
1.3%
3619
 
1.1%
4592
 
1.0%
5428
 
0.7%
6380
 
0.6%
8323
 
0.5%
10314
 
0.5%
7295
 
0.5%
Other values (788)6542
 
11.1%
ValueCountFrequency (%)
047312
80.3%
11350
 
2.3%
2764
 
1.3%
3619
 
1.1%
4592
 
1.0%
5428
 
0.7%
6380
 
0.6%
7295
 
0.5%
8323
 
0.5%
9217
 
0.4%
ValueCountFrequency (%)
38721
< 0.1%
37321
< 0.1%
37281
< 0.1%
37151
< 0.1%
37061
< 0.1%
36951
< 0.1%
35001
< 0.1%
33711
< 0.1%
32011
< 0.1%
31851
< 0.1%

forecast_3_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1536
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.25288956
Minimum0
Maximum15600
Zeros34863
Zeros (%)59.2%
Negative0
Negative (%)0.0%
Memory size460.4 KiB
2021-10-22T00:04:56.698785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile335
Maximum15600
Range15600
Interquartile range (IQR)12

Descriptive statistics

Standard deviation497.8202309
Coefficient of variation (CV)5.705487044
Kurtosis253.2248987
Mean87.25288956
Median Absolute Deviation (MAD)0
Skewness13.55471877
Sum5140853
Variance247824.9823
MonotonicityNot monotonic
2021-10-22T00:04:56.824201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034863
59.2%
11354
 
2.3%
21243
 
2.1%
51056
 
1.8%
4962
 
1.6%
3885
 
1.5%
10774
 
1.3%
6743
 
1.3%
8611
 
1.0%
12603
 
1.0%
Other values (1526)15825
26.9%
ValueCountFrequency (%)
034863
59.2%
11354
 
2.3%
21243
 
2.1%
3885
 
1.5%
4962
 
1.6%
51056
 
1.8%
6743
 
1.3%
7457
 
0.8%
8611
 
1.0%
9374
 
0.6%
ValueCountFrequency (%)
156001
< 0.1%
155521
< 0.1%
147301
< 0.1%
143501
< 0.1%
140001
< 0.1%
136081
< 0.1%
133201
< 0.1%
131401
< 0.1%
126301
< 0.1%
125431
< 0.1%

forecast_6_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2090
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162.1785672
Minimum0
Maximum29200
Zeros31574
Zeros (%)53.6%
Negative0
Negative (%)0.0%
Memory size460.4 KiB
2021-10-22T00:04:56.966180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q326
95-th percentile606
Maximum29200
Range29200
Interquartile range (IQR)26

Descriptive statistics

Standard deviation913.3398768
Coefficient of variation (CV)5.631692847
Kurtosis249.2752524
Mean162.1785672
Median Absolute Deviation (MAD)0
Skewness13.43519199
Sum9555399
Variance834189.7305
MonotonicityNot monotonic
2021-10-22T00:04:57.084031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
031574
53.6%
11109
 
1.9%
21077
 
1.8%
3986
 
1.7%
4924
 
1.6%
5864
 
1.5%
6766
 
1.3%
10757
 
1.3%
8681
 
1.2%
7640
 
1.1%
Other values (2080)19541
33.2%
ValueCountFrequency (%)
031574
53.6%
11109
 
1.9%
21077
 
1.8%
3986
 
1.7%
4924
 
1.6%
5864
 
1.5%
6766
 
1.3%
7640
 
1.1%
8681
 
1.2%
9406
 
0.7%
ValueCountFrequency (%)
292001
< 0.1%
290001
< 0.1%
262501
< 0.1%
259301
< 0.1%
256201
< 0.1%
255001
< 0.1%
252001
< 0.1%
244721
< 0.1%
222121
< 0.1%
222001
< 0.1%

forecast_9_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2522
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean237.7437838
Minimum0
Maximum43000
Zeros29856
Zeros (%)50.7%
Negative0
Negative (%)0.0%
Memory size460.4 KiB
2021-10-22T00:04:57.220510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q340
95-th percentile900
Maximum43000
Range43000
Interquartile range (IQR)40

Descriptive statistics

Standard deviation1345.784311
Coefficient of variation (CV)5.660649836
Kurtosis250.786079
Mean237.7437838
Median Absolute Deviation (MAD)0
Skewness13.54096418
Sum14007626
Variance1811135.412
MonotonicityNot monotonic
2021-10-22T00:04:57.334619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
029856
50.7%
11003
 
1.7%
2994
 
1.7%
4893
 
1.5%
3889
 
1.5%
5844
 
1.4%
6809
 
1.4%
10793
 
1.3%
8686
 
1.2%
12641
 
1.1%
Other values (2512)21511
36.5%
ValueCountFrequency (%)
029856
50.7%
11003
 
1.7%
2994
 
1.7%
3889
 
1.5%
4893
 
1.5%
5844
 
1.4%
6809
 
1.4%
7557
 
0.9%
8686
 
1.2%
9429
 
0.7%
ValueCountFrequency (%)
430001
< 0.1%
407201
< 0.1%
388201
< 0.1%
385801
< 0.1%
360001
< 0.1%
357301
< 0.1%
357001
< 0.1%
342001
< 0.1%
334211
< 0.1%
334081
< 0.1%

sales_1_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct961
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.50520206
Minimum0
Maximum4934
Zeros30495
Zeros (%)51.8%
Negative0
Negative (%)0.0%
Memory size460.4 KiB
2021-10-22T00:04:57.457082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile101
Maximum4934
Range4934
Interquartile range (IQR)6

Descriptive statistics

Standard deviation136.6048481
Coefficient of variation (CV)5.355960238
Kurtosis280.0051028
Mean25.50520206
Median Absolute Deviation (MAD)0
Skewness13.88048916
Sum1502741
Variance18660.88452
MonotonicityNot monotonic
2021-10-22T00:04:57.588530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
030495
51.8%
15316
 
9.0%
23146
 
5.3%
32191
 
3.7%
41624
 
2.8%
51343
 
2.3%
61046
 
1.8%
7868
 
1.5%
8691
 
1.2%
9610
 
1.0%
Other values (951)11589
 
19.7%
ValueCountFrequency (%)
030495
51.8%
15316
 
9.0%
23146
 
5.3%
32191
 
3.7%
41624
 
2.8%
51343
 
2.3%
61046
 
1.8%
7868
 
1.5%
8691
 
1.2%
9610
 
1.0%
ValueCountFrequency (%)
49341
< 0.1%
46271
< 0.1%
43261
< 0.1%
42971
< 0.1%
40191
< 0.1%
39261
< 0.1%
38171
< 0.1%
36851
< 0.1%
35581
< 0.1%
35111
< 0.1%

sales_3_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1804
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.25299139
Minimum0
Maximum13554
Zeros23658
Zeros (%)40.2%
Negative0
Negative (%)0.0%
Memory size460.4 KiB
2021-10-22T00:04:57.710885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q318
95-th percentile325
Maximum13554
Range13554
Interquartile range (IQR)18

Descriptive statistics

Standard deviation430.1854963
Coefficient of variation (CV)5.230028586
Kurtosis223.8563959
Mean82.25299139
Median Absolute Deviation (MAD)2
Skewness12.66619534
Sum4846264
Variance185059.5612
MonotonicityNot monotonic
2021-10-22T00:04:57.844374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
023658
40.2%
14518
 
7.7%
22997
 
5.1%
32049
 
3.5%
41625
 
2.8%
51389
 
2.4%
61152
 
2.0%
7950
 
1.6%
8877
 
1.5%
9760
 
1.3%
Other values (1794)18944
32.2%
ValueCountFrequency (%)
023658
40.2%
14518
 
7.7%
22997
 
5.1%
32049
 
3.5%
41625
 
2.8%
51389
 
2.4%
61152
 
2.0%
7950
 
1.6%
8877
 
1.5%
9760
 
1.3%
ValueCountFrequency (%)
135541
< 0.1%
127031
< 0.1%
123921
< 0.1%
122721
< 0.1%
122091
< 0.1%
111801
< 0.1%
111271
< 0.1%
105571
< 0.1%
105101
< 0.1%
102971
< 0.1%

sales_6_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2528
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean163.3491403
Minimum0
Maximum26299
Zeros20066
Zeros (%)34.1%
Negative0
Negative (%)0.0%
Memory size460.4 KiB
2021-10-22T00:04:57.978448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q335
95-th percentile644.1
Maximum26299
Range26299
Interquartile range (IQR)35

Descriptive statistics

Standard deviation860.7644959
Coefficient of variation (CV)5.269476742
Kurtosis228.0201025
Mean163.3491403
Median Absolute Deviation (MAD)4
Skewness12.82974373
Sum9624368
Variance740915.5173
MonotonicityNot monotonic
2021-10-22T00:04:58.104633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
020066
34.1%
14116
 
7.0%
22757
 
4.7%
31899
 
3.2%
41567
 
2.7%
51268
 
2.2%
61124
 
1.9%
8881
 
1.5%
7867
 
1.5%
9726
 
1.2%
Other values (2518)23648
40.1%
ValueCountFrequency (%)
020066
34.1%
14116
 
7.0%
22757
 
4.7%
31899
 
3.2%
41567
 
2.7%
51268
 
2.2%
61124
 
1.9%
7867
 
1.5%
8881
 
1.5%
9726
 
1.2%
ValueCountFrequency (%)
262991
< 0.1%
251791
< 0.1%
239681
< 0.1%
238721
< 0.1%
235601
< 0.1%
232751
< 0.1%
220801
< 0.1%
219451
< 0.1%
216101
< 0.1%
214421
< 0.1%

sales_9_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct3062
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean248.471936
Minimum0
Maximum42656
Zeros18134
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size460.4 KiB
2021-10-22T00:04:58.234362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q352
95-th percentile972
Maximum42656
Range42656
Interquartile range (IQR)52

Descriptive statistics

Standard deviation1321.245573
Coefficient of variation (CV)5.317484117
Kurtosis255.5284637
Mean248.471936
Median Absolute Deviation (MAD)6
Skewness13.33812818
Sum14639718
Variance1745689.865
MonotonicityNot monotonic
2021-10-22T00:04:58.364475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
018134
30.8%
13823
 
6.5%
22615
 
4.4%
31878
 
3.2%
41484
 
2.5%
51265
 
2.1%
61097
 
1.9%
7887
 
1.5%
8777
 
1.3%
9729
 
1.2%
Other values (3052)26230
44.5%
ValueCountFrequency (%)
018134
30.8%
13823
 
6.5%
22615
 
4.4%
31878
 
3.2%
41484
 
2.5%
51265
 
2.1%
61097
 
1.9%
7887
 
1.5%
8777
 
1.3%
9729
 
1.2%
ValueCountFrequency (%)
426561
< 0.1%
425261
< 0.1%
411111
< 0.1%
390661
< 0.1%
388041
< 0.1%
381941
< 0.1%
374721
< 0.1%
374481
< 0.1%
359761
< 0.1%
350781
< 0.1%

min_bank
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct917
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.15448327
Minimum0
Maximum3390
Zeros30846
Zeros (%)52.4%
Negative0
Negative (%)0.0%
Memory size460.4 KiB
2021-10-22T00:04:58.498630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile115
Maximum3390
Range3390
Interquartile range (IQR)3

Descriptive statistics

Standard deviation129.5775772
Coefficient of variation (CV)4.954316086
Kurtosis195.1081789
Mean26.15448327
Median Absolute Deviation (MAD)0
Skewness12.01563522
Sum1540996
Variance16790.34852
MonotonicityNot monotonic
2021-10-22T00:04:58.734391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
030846
52.4%
18446
 
14.3%
23778
 
6.4%
31257
 
2.1%
4850
 
1.4%
5603
 
1.0%
6437
 
0.7%
7343
 
0.6%
15285
 
0.5%
8273
 
0.5%
Other values (907)11801
 
20.0%
ValueCountFrequency (%)
030846
52.4%
18446
 
14.3%
23778
 
6.4%
31257
 
2.1%
4850
 
1.4%
5603
 
1.0%
6437
 
0.7%
7343
 
0.6%
8273
 
0.5%
9210
 
0.4%
ValueCountFrequency (%)
33901
< 0.1%
33031
< 0.1%
31591
< 0.1%
31051
< 0.1%
30831
< 0.1%
30452
< 0.1%
30231
< 0.1%
30211
< 0.1%
30051
< 0.1%
29961
< 0.1%

potential_issue
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size460.4 KiB
0
58858 
1
 
61

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
058858
99.9%
161
 
0.1%

Length

2021-10-22T00:04:58.857196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-22T00:04:58.927046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
058858
99.9%
161
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

pieces_past_due
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct171
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9727252669
Minimum0
Maximum638
Zeros57335
Zeros (%)97.3%
Negative0
Negative (%)0.0%
Memory size460.4 KiB
2021-10-22T00:04:59.004311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum638
Range638
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13.79268948
Coefficient of variation (CV)14.17942964
Kurtosis796.3752099
Mean0.9727252669
Median Absolute Deviation (MAD)0
Skewness25.47929635
Sum57312
Variance190.2382832
MonotonicityNot monotonic
2021-10-22T00:04:59.124488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
057335
97.3%
1233
 
0.4%
2149
 
0.3%
488
 
0.1%
372
 
0.1%
565
 
0.1%
1261
 
0.1%
653
 
0.1%
1045
 
0.1%
843
 
0.1%
Other values (161)775
 
1.3%
ValueCountFrequency (%)
057335
97.3%
1233
 
0.4%
2149
 
0.3%
372
 
0.1%
488
 
0.1%
565
 
0.1%
653
 
0.1%
739
 
0.1%
843
 
0.1%
926
 
< 0.1%
ValueCountFrequency (%)
6381
 
< 0.1%
6002
< 0.1%
5441
 
< 0.1%
5402
< 0.1%
5201
 
< 0.1%
5003
< 0.1%
4641
 
< 0.1%
4551
 
< 0.1%
4501
 
< 0.1%
4461
 
< 0.1%

perf_6_month_avg
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7717668324
Minimum0
Maximum1
Zeros1600
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size460.4 KiB
2021-10-22T00:04:59.263729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.19
Q10.69
median0.84
Q30.97
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.28

Descriptive statistics

Standard deviation0.2449000802
Coefficient of variation (CV)0.3173239247
Kurtosis1.861181803
Mean0.7717668324
Median Absolute Deviation (MAD)0.13
Skewness-1.504102324
Sum45471.73
Variance0.0599760493
MonotonicityNot monotonic
2021-10-22T00:04:59.394442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.995137
 
8.7%
14704
 
8.0%
0.733963
 
6.7%
0.983150
 
5.3%
0.972374
 
4.0%
0.781782
 
3.0%
0.821601
 
2.7%
01600
 
2.7%
0.951553
 
2.6%
0.961479
 
2.5%
Other values (91)31576
53.6%
ValueCountFrequency (%)
01600
2.7%
0.0115
 
< 0.1%
0.0258
 
0.1%
0.0330
 
0.1%
0.0427
 
< 0.1%
0.0579
 
0.1%
0.0644
 
0.1%
0.0799
 
0.2%
0.0880
 
0.1%
0.0965
 
0.1%
ValueCountFrequency (%)
14704
8.0%
0.995137
8.7%
0.983150
5.3%
0.972374
4.0%
0.961479
 
2.5%
0.951553
 
2.6%
0.941370
 
2.3%
0.931236
 
2.1%
0.92868
 
1.5%
0.911147
 
1.9%

perf_12_month_avg
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7685442726
Minimum0
Maximum1
Zeros1232
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size460.4 KiB
2021-10-22T00:04:59.524272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.23
Q10.68
median0.82
Q30.95
95-th percentile0.99
Maximum1
Range1
Interquartile range (IQR)0.27

Descriptive statistics

Standard deviation0.2351967542
Coefficient of variation (CV)0.3060288946
Kurtosis2.002128514
Mean0.7685442726
Median Absolute Deviation (MAD)0.13
Skewness-1.527267546
Sum45281.86
Variance0.05531751319
MonotonicityNot monotonic
2021-10-22T00:04:59.654264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.994707
 
8.0%
0.784160
 
7.1%
0.983442
 
5.8%
0.972471
 
4.2%
0.962289
 
3.9%
0.661979
 
3.4%
0.91752
 
3.0%
0.951684
 
2.9%
11540
 
2.6%
0.791493
 
2.5%
Other values (91)33402
56.7%
ValueCountFrequency (%)
01232
2.1%
0.0191
 
0.2%
0.0225
 
< 0.1%
0.0328
 
< 0.1%
0.0442
 
0.1%
0.0534
 
0.1%
0.0637
 
0.1%
0.0755
 
0.1%
0.0856
 
0.1%
0.09100
 
0.2%
ValueCountFrequency (%)
11540
 
2.6%
0.994707
8.0%
0.983442
5.8%
0.972471
4.2%
0.962289
3.9%
0.951684
 
2.9%
0.941424
 
2.4%
0.931207
 
2.0%
0.921129
 
1.9%
0.911129
 
1.9%

local_bo_qty
Real number (ℝ≥0)

ZEROS

Distinct92
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2821161255
Minimum0
Maximum127
Zeros57337
Zeros (%)97.3%
Negative0
Negative (%)0.0%
Memory size460.4 KiB
2021-10-22T00:04:59.784154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum127
Range127
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.333715762
Coefficient of variation (CV)11.81682102
Kurtosis435.8098306
Mean0.2821161255
Median Absolute Deviation (MAD)0
Skewness18.94450855
Sum16622
Variance11.11366078
MonotonicityNot monotonic
2021-10-22T00:04:59.914366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
057337
97.3%
1475
 
0.8%
2204
 
0.3%
3143
 
0.2%
4110
 
0.2%
565
 
0.1%
658
 
0.1%
744
 
0.1%
837
 
0.1%
1030
 
0.1%
Other values (82)416
 
0.7%
ValueCountFrequency (%)
057337
97.3%
1475
 
0.8%
2204
 
0.3%
3143
 
0.2%
4110
 
0.2%
565
 
0.1%
658
 
0.1%
744
 
0.1%
837
 
0.1%
922
 
< 0.1%
ValueCountFrequency (%)
1271
 
< 0.1%
1191
 
< 0.1%
1021
 
< 0.1%
1004
< 0.1%
981
 
< 0.1%
971
 
< 0.1%
961
 
< 0.1%
931
 
< 0.1%
921
 
< 0.1%
911
 
< 0.1%

deck_risk
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size460.4 KiB
0
48287 
1
10632 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
048287
82.0%
110632
 
18.0%

Length

2021-10-22T00:05:00.044606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-22T00:05:00.114208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
048287
82.0%
110632
 
18.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

oe_constraint
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size460.4 KiB
0
58908 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
058908
> 99.9%
111
 
< 0.1%

Length

2021-10-22T00:05:00.194475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-22T00:05:00.274307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
058908
> 99.9%
111
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ppap_risk
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size460.4 KiB
0
51794 
1
7125 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
051794
87.9%
17125
 
12.1%

Length

2021-10-22T00:05:00.346231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-22T00:05:00.417706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
051794
87.9%
17125
 
12.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

stop_auto_buy
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size460.4 KiB
1
57870 
0
 
1049

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
157870
98.2%
01049
 
1.8%

Length

2021-10-22T00:05:00.494108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-22T00:05:00.571067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
157870
98.2%
01049
 
1.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

rev_stop
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size460.4 KiB
0
58904 
1
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
058904
> 99.9%
115
 
< 0.1%

Length

2021-10-22T00:05:00.650548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-22T00:05:00.714839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
058904
> 99.9%
115
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size460.4 KiB
0
49624 
1
9295 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
049624
84.2%
19295
 
15.8%

Length

2021-10-22T00:05:00.794420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-22T00:05:00.867451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
049624
84.2%
19295
 
15.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-10-22T00:04:51.725052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:17.869092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:20.144121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:22.464495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:24.736112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:27.114296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:29.524109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:31.767664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:33.947365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:36.054310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:38.414673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:40.542754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:42.794491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:44.985052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:47.294268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:49.454349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:51.973462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:18.014030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:20.274234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:22.585619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:24.878154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:27.247166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:29.652869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:31.890844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:34.078193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:36.186795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:38.537726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:40.669154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:42.924243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:45.108480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:47.414505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:49.584749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:52.124566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:18.164245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:20.436963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:22.844348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:25.044136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:27.394656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:29.794689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:32.024284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:34.221506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:36.330991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:38.674325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:40.814619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:43.066871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:45.252165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:47.555680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:49.734811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:52.255079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:18.324383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:20.584176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:22.972990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:25.192964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:27.535377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:29.928243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:32.157120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:34.344057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:36.464312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:38.804404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:40.944521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:43.204483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:45.385412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:47.681306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:49.874327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:52.406952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:18.474462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:20.741837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:23.108916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:25.355425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:27.804119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:30.080928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:32.294252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:34.487036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:36.615288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:38.947215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:41.094324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:43.344233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:45.528344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:47.824564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:50.029384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:52.567408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:18.626091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:20.904471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:23.254585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:25.518743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:27.955707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:30.239185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:32.434314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:34.630723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:36.768140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:39.091441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:41.244054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:43.495378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:45.684276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:47.967081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:50.189422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:52.711419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:18.764320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:21.054330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:23.395143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:25.671209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:28.099367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:30.376091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:32.679202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:34.764081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:36.914213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:39.224391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:41.389315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:43.634401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:45.831255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:48.100908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:50.334476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:52.839227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:18.902974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:21.188940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:23.524223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:25.823244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:28.240492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:30.510570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:32.794400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:34.885999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:37.044454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:39.346771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:41.517123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:43.764134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:45.969438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:48.234157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:50.464249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:52.965381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:19.044135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:21.322824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:23.654073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:25.965378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:28.385225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:30.654283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:32.922397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:35.014240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:37.185536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:39.474230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:41.648996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:43.897667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:46.106370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:48.355752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:50.600333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:53.098774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:19.195808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:21.468800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:23.794771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:26.124318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:28.534363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:30.808772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:33.057258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:35.151354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:37.444440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:39.611246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:41.794206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:44.041839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:46.248115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:48.507722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:50.754400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:53.240832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:19.340450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:21.610263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:23.924344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:26.260153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:28.672791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:30.940232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:33.180624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:35.276400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:37.574884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:39.744547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:41.914578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:44.174481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:46.379682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:48.644538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:50.892249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:53.374410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:19.464336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:21.734905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:24.044440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:26.396026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:28.807096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:31.064172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:33.299368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:35.399052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:37.708234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:39.874191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:42.036109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:44.307483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:46.504183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:48.771202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:51.026241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:53.522795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:19.605843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:21.888739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:24.176703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:26.539689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:28.954215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:31.215928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:33.425561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:35.534137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:37.854452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:40.014543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:42.284220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:44.449394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:46.644141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:48.911211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:51.167211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:53.666935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:19.744823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:22.040700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:24.317733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:26.684189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:29.094454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:31.364289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:33.557035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:35.666017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:37.995031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:40.144390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:42.414201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:44.584563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:46.778559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:49.045004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:51.317357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:53.809138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:19.886106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:22.175308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:24.454461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:26.826994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:29.236876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:31.494457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:33.684534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:35.794583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:38.138094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:40.276687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:42.536160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:44.718160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:47.025749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:49.168166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:51.450396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:53.948860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:20.014412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:22.317127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:24.594541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:26.971149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:29.378951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:31.625082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:33.815547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:35.922251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:38.274641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:40.408027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:42.669546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:44.854538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:47.159361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:49.308673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T00:04:51.583310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-10-22T00:05:00.974369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-22T00:05:01.464382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-22T00:05:01.831534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-22T00:05:02.166281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-10-22T00:05:02.354338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-22T00:04:54.229151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-22T00:04:55.192241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

skunational_invlead_timein_transit_qtyforecast_3_monthforecast_6_monthforecast_9_monthsales_1_monthsales_3_monthsales_6_monthsales_9_monthmin_bankpotential_issuepieces_past_dueperf_6_month_avgperf_12_month_avglocal_bo_qtydeck_riskoe_constraintppap_riskstop_auto_buyrev_stopwent_on_backorder
0242000000000000.180.310100100
15216000000001000.310.400000100
2744000000000000.730.780000100
3909011100000000.860.690000100
410300800000401001227000.000.000000100
511112000001222000.770.730100100
61696358260008826753887597000.120.120000100
71948000017792000.620.620101100
8202241618365412036510000.730.780000100
92221891132183233536114737755862000.850.820000100

Last rows

skunational_invlead_timein_transit_qtyforecast_3_monthforecast_6_monthforecast_9_monthsales_1_monthsales_3_monthsales_6_monthsales_9_monthmin_bankpotential_issuepieces_past_dueperf_6_month_avgperf_12_month_avglocal_bo_qtydeck_riskoe_constraintppap_riskstop_auto_buyrev_stopwent_on_backorder
589098416042055526661000.970.980101100
589108416148000000110000.990.960000100
5891184162792000061116230001.000.950000100
589128416322000000000000.420.360101100
5891384164420244872162946690000.980.970000101
589148416511800002617266000.540.640100100
589158416608045723450000.760.770000101
589168416702011100000000.090.060000101
589178416964000000000000.770.800100100
58918841710120505050000000500.630.630100100